Abstract

We explore the match of a computational information foraging model to participant data on multi-page web search tasks and find its correlation on several important metrics to be too low to be used with confidence in the evaluation of user-interface designs. We examine the points of mismatch to inspire changes to the model in how it calculates information scent scores and how it assesses the utility of backing up from a lower-level page to a higher-level page. The outcome is a new model that qualitatively matches participant behavior better than the original model, has utility equations more appealing to “common sense” than the original equations, and significantly improves the correlation between model and participant data on our metrics